The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS

A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise $\sigma ^{o}$ bias correction method. This method makes use of both ancillary data from numerical weather prediction models semiempirical geophysical model function. Care taken, so the maintains CyGNSS signal sensitivity. Both are removed after correction. Long-term downward trend, observed throughout mission, greatly reduced. Using corrected measurements, wind retrieval also presented its product thoroughly assessed for three-year period against European Centre Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane research forecasting (HWRF) model, stepped frequency microwave radiometer (SFMR) winds. The overall speed standard deviation error (stde) ECMWF 0.16 1.19 m/s, while these −0.11 1.12 m/s ASCAT respectively. same metrics AMSR-2/GMI/WindSat (combined) −0.19 1.11 stde soil moisture active passive (SMAP) −0.38 1.90 In tropical environment, HWRF −0.54 2.90 −4.71 5.88 with SFMR. Finally, performance gauged presence rain. Below 10 between increases as rain rate increases. Between 15 biases mostly absent. Above results inconclusive due to low number collocated samples. Overall, exhibits consistency reliability, showing promise using GNSS-R derived winds operational purposes.